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The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the written permission of IASA is strictly prohibited. © IASA 2010
A Day in the Life
n Designing the Internet of Things by Adrian McEwen & Hakim Cassimally n Wake late – alarm has checked train schedule
n Tablet blinking on bottle lights for reminder mails doctor
n Umbrella lit up going to rain
n At station phone notifies family
n Training shoes update cloud application and doctor
n Integrates with online shopping to map calories
IASA is
n a non-profit professional association
n run by architects
n for all IT architects
n centrally governed and locally run
n technology and vendor agnostic
The use, disclosure, reproduction, modification,
transfer, or transmittal of this work without the written
permission of IASA is strictly prohibited. © IASA 2009
Topics
n What is Cloud and IoT?
n What is the relationship between Cloud and IoT?
n Where is the ‘Smart’ in Smart Cloud and Smart IoT?
n What is valuable about Cloud and IoT?
n How to include smart system thinking into designs
n How to get started with smart tools like inferencing, fuzzy, neural and other technologies
n When to think smart and when to avoid
n Possible outcomes to strive for today in preparing your architecture for the age of smart systems.
Cloud and IoT
n Everything will have identity whether simple or complex n ‘who is my phone’
n Everything will be able to act physically or virtually or both n ‘what can my phone do’
n Everything will be able to act dependently or independently or both n ‘what can my phone do with permission or without it’
n Everything will have more or less responsibility for everything it interacts with n ‘what does my phone do to other devices|people|systems’
Cloud
n The umbrella term for anything available over a network
n Relevant attributes which typify and classify architectures include n Public or private
n Virtualized or non-virtualized
n Service oriented or person oriented
n Hardware oriented or platform oriented or software oriented
n Organizationally oriented or personally oriented
n Secure or unsecure
n Paid or free
n Paid by quality attribute or paid by operational attribute
n Guaranteed or unguaranteed
Internet of Things
n Identifying all physical and virtual objects on a network
n Relevant attributes which will typify and classify architectures may include n Type of IoT identity (hardware, network, software, service,
invoker, agent, intelligent agent, independent intelligent agent, provocateur)
n Size or scope of object (molecular -> planetary)
n Data type/volume consumption/production
n Power consumption/production
n Location and Mobility
n Object interaction power in virtual, physical or both
n Intention and Autonomy
Proposed Hierarchy of IoT Identities n Provocateur - Intelligent agent with intention (human level)
n Independent Intelligent Agent - Intelligent agent acting without permission
n Intelligent Agent – Agent with a degree of reasoning capacity
n Agent – Invoker which changes addresses in some way
n Invoker – Service which calls other services
n Service – Software object which returns a complex response
n Software – Network object which returns a simple response
n Network – An object which is addressable over a network
n Hardware – An object which is identifiable over a network
Concepts and Relationships
n Cloud is the raw network access mechanism
n IoT is the type of things accessible
n Understanding these relationships requires a much more sophisticated ontology and series of reference points
Why Is Smart Required for IoT and Cloud?
n Volumes of data require more advanced searching, analysis and transformation techniques
n Automation and availability of physical and virtual services require significantly complex process orchestration and optimization
n Competitive advantage in business will continue to require more awareness and ability within smaller opportunities
n All businesses of all sizes are technology businesses
n Both human and non-human provocateurs will take advantage of less sophisticated provocateurs
How is Smart Implemented Now
n Advanced Search – Genetic, Graph Theory
n Inferencing (Deductive, Inductive)
n Fuzzy Reasoning
n Optimization
n Learning
n Interpreting and Language
n Negotiation
Searching for Information
n Our lives and companies are run with information
n Information has to be constructed from data and context
n There is more data and information in the world than we can process
n Intelligent search is key to our ability to make use of information
n Common applications: business intelligence, lifestyle optimization, interest optimization
The Rules We Live By
n Most companies have large numbers of commonly modified rules
n Inferencing allows us to n deduce new information within context (forward-chaining)
n induce information from existing data (backward-chaining)
n Common Applications: Insurance rates and converage, retail pricing and discounts, purchase decisions, lifestyle choices n “If the train is late let me sleep in”
Fuzzy Reasoning and Controllers
n Humans and business work on ‘fuzzy definitions’ which is simply that most things are both true and not true n “It is cold in Sweden” may be true to a Texan but not an Eskimo! n “A cup is also a bowl” can be more or less true n “That hotel is extremely expensive” for me but Bill Gates?
n Allows our devices to be more precise and selective in decision making and reasoning n “Pre-heat the car when it is very cold” n “We buy very high quality business supplies”
n Common Applications: Energy utilization, mechanical controllers, human definitional input
Optimization
n Business processes, graph navigation, optimal path traversal, and business integration all involve process optimizations
n Multi-processes integration beyond the simplicity of a single service (physical or virtual) control much of our lives
n Utilization of embedded process engines and optimization allows for maximum flexibility of physical and virtual agents
n Common Applications: multi-partner business transactions, automated delivery systems, personal travel itineraries, multi-device automation
Learning
n More and more data and choice is available to system software
n As automation and autonomy become ubiquitous training in desired outcomes is necessary for personal and business
n The vast amount of data and information requires grouping, characterizing and classifying
n Neural networks and decision trees
n Common applications: Food, travel and personal preferences, natural language processing, optimal energy input/output, security threat detection
Thing to Thing Communication
n Language, dialect, grammar, vocabulary and pronunciation are all relevant in IoT communications and configuration
n Knowledge and language ontology and dictionary will be essential to self-configuration (and therefore adoption)
n This may be the single most difficult task in the IoT n Even humans struggle with this constantly n ‘Molecular’ data element combinations are not solidified (what is
an address, a name, a birthday)
n Common applications: Thing configuration and communication, business analytics, service orchestration, personal identity management (pay for use)
Negotiation
n As systems begin to represent us there is more and more conflict n “What is the best price we can get for pencils for employees”
n Using negotiation techniques to avoid conflict with game theory
n Common applications: Device resource allocation and utilization, purchasing
Considering Value and Risk
n Value to Who? n Individuals
n Governments and NGOs
n Vendors and Service Integrators
n For Profit – non-vendor
n What type of Value n Lifestyle|Social Value
n Financial Value
n Customer|Operational Value
n Societal|Human Value
n Risk to Who? n Individual
n Corporation
n Governments
n What type of Risk? n Physical
n Financial
n Societal
How Smart Becomes Value
n There is a world of ‘new’ objects to sell to the world
n There is an unlimited number of ways to incorporate new inventions into multiple channels, services and ‘products’ n Learning about your customers and partners
n Dynamically allocating resources and processes
n Optimized pathing
n Planning and forecasting
n Configuration management and ease of use
n Human interaction and reasoning
Architecture Value
• Profitability • Constituent Value • Reuse • Grow Market Size • Grow Market Quality
What is “creates value”? What is Good? suitable or efficient for a purpose beneficial or advantageous
Value Questions
n Financial Value n How do our customers buy from us?
n When does a person ‘have’ to be involved?
n How do our partners supply us?
n When do our customers have to think?
n When do our employees have to use a best guess or experience?
n Are there times we ‘diagnose’ a problem?
n How can our systems interact on long-lasting complex transactions?
What does Smart Mean Tomorrow
n We must begin to consider systems as more than software services n Autonomy – the degree to which systems can act without
permission
n Power (to influence) – the amount of influence or size of outcomes a system can achieve
n Resources (to command and use) – the size and makeup of objects a system may use
n Motivation – as systems gain more power and autonomy we will need to understand
n Combat – when systems with autonomy, power and resources disagree about outcomes
Resources
n Books n Designing the Internet of Things
n Practical Artificial Intelligence Programming with Java
n Rethinking the Internet of Things
n IoT – Global Technological and Societal Trends
n Tools/Frameworks n Drools
n Weka
n JFuzzyLogic
n Fuzzylite
n Gambit
Skill Taxonomy
The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the written permission of IASA is strictly prohibited. © IASA 2009
Questions